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Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data

MOTIVATION: An important goal of concentration–response studies in toxicology is to determine an ‘alert’ concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations...

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Autores principales: Kappenberg, Franziska, Grinberg, Marianna, Jiang, Xiaoqi, Kopp-Schneider, Annette, Hengstler, Jan G, Rahnenführer, Jörg
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8337003/
https://www.ncbi.nlm.nih.gov/pubmed/33515236
http://dx.doi.org/10.1093/bioinformatics/btab043
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author Kappenberg, Franziska
Grinberg, Marianna
Jiang, Xiaoqi
Kopp-Schneider, Annette
Hengstler, Jan G
Rahnenführer, Jörg
author_facet Kappenberg, Franziska
Grinberg, Marianna
Jiang, Xiaoqi
Kopp-Schneider, Annette
Hengstler, Jan G
Rahnenführer, Jörg
author_sort Kappenberg, Franziska
collection PubMed
description MOTIVATION: An important goal of concentration–response studies in toxicology is to determine an ‘alert’ concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations. Alternatively, a parametric curve is fitted to the data that describes the relationship between concentration and response. For a prespecified effect level, both an absolute estimate of the alert concentration and an estimate of the lowest concentration where the effect level is exceeded significantly are of interest. RESULTS: In a simulation study for gene expression data, we compared the observation-based and the model-based approach for both absolute and significant exceedance of the prespecified effect level. Results show that, compared to the observation-based approach, the model-based approach overestimates the true alert concentration less often and more frequently leads to a valid estimate, especially for genes with large variance. AVAILABILITY AND IMPLEMENTATION: The code used for the simulation studies is available via the GitHub repository: https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-83370032021-08-09 Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data Kappenberg, Franziska Grinberg, Marianna Jiang, Xiaoqi Kopp-Schneider, Annette Hengstler, Jan G Rahnenführer, Jörg Bioinformatics Original Papers MOTIVATION: An important goal of concentration–response studies in toxicology is to determine an ‘alert’ concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations. Alternatively, a parametric curve is fitted to the data that describes the relationship between concentration and response. For a prespecified effect level, both an absolute estimate of the alert concentration and an estimate of the lowest concentration where the effect level is exceeded significantly are of interest. RESULTS: In a simulation study for gene expression data, we compared the observation-based and the model-based approach for both absolute and significant exceedance of the prespecified effect level. Results show that, compared to the observation-based approach, the model-based approach overestimates the true alert concentration less often and more frequently leads to a valid estimate, especially for genes with large variance. AVAILABILITY AND IMPLEMENTATION: The code used for the simulation studies is available via the GitHub repository: https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-01-30 /pmc/articles/PMC8337003/ /pubmed/33515236 http://dx.doi.org/10.1093/bioinformatics/btab043 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Papers
Kappenberg, Franziska
Grinberg, Marianna
Jiang, Xiaoqi
Kopp-Schneider, Annette
Hengstler, Jan G
Rahnenführer, Jörg
Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data
title Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data
title_full Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data
title_fullStr Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data
title_full_unstemmed Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data
title_short Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data
title_sort comparison of observation-based and model-based identification of alert concentrations from concentration–expression data
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8337003/
https://www.ncbi.nlm.nih.gov/pubmed/33515236
http://dx.doi.org/10.1093/bioinformatics/btab043
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